The site

Grotta di Castelcivita and the Aurignacian stratigraphic sequence

Grotta di Castelcivita (hereafter Castelcivita; Salerno, Campania, Southern Italy) is a cave site situated at an elevation of 94 meters above sea level, nestled at the base of the Alburni massif, near the right bank of the Calore River (40.49563600N, 015.20922177E; see Fig. 1). This cave is part of a karst system that spans two primary levels, comprising an extensive network of tunnels and chambers, with a total length exceeding 5 kilometers (Cafaro et al., 2016). Systematic excavations were first conducted from 1975 to 1988, led by P. Gambassini of the Research Unit of Prehistory and Anthropology at the University of Siena (Gambassini, 1997). Subsequently, fieldwork was resumed in 2015 by the same research unit, under the direction of A. Ronchitelli and A. Moroni, and continuing to the present year, in collaboration with the “Soprintendenza Archeologia, Belle Arti e Paesaggio per le province di Salerno e Avellino”. The archaeological deposit is situated at the actual cave entrance and presently covers an excavated area of 35 square meters, of which Gambassini excavated 14 in the last century. Before the systematic investigations in 1975, nearly 6 square meters were excavated by looters (see Fig. 2b–d), leaving a deep large pit down to the base of the anthropogenic sequence. The destination of the archaeological materials originating from this area is presently unknown. The archaeological sequence spans a depth of 3.4 meters and contains multiple layers attributed to the late Mousterian (cgr, gar, lower-rsi), the Uluzzian (upper-rsi, pie, rpi, and rsa’‘), and the Aurignacian (rsa’, gic, and ars; see Fig. 2a). Layer ars is overlaid by a decimeter-thick layer of tephra (Giaccio et al., 2008), that corresponds to the well-known Campanian Ignimbrite (CI; Fedele et al., 2004), one of the largest Late Quaternary explosive events and an example of super-eruption (Sparks et al., 2005). This event was recently dated to 39.85 ± 0.14 ka BP (Giaccio et al., 2017) and originated from the largest eruption of the Phlegrean Field caldera. Fallout deposits connected with this event were recognized in many continental, marine, and cave successions of Europe and Asia and, consequently, it is one of the most important temporal and stratigraphic markers of western Eurasia (d’Errico & Banks, 2015). This tephra layer in the succession of Castelcivita represents a terminus ante quem for the site’s last human occupations. Layer CI is in turn overlaid by a multi-layered flowstone with embedded thin layers of volcanic ashes that seal the entire stratigraphic sequence. These thin layers were probably connected to the surficial reworking of CI deposits, subsequently redeposited within the cave. In this paper, our focus lies on the Aurignacian sequence, which follows the last Uluzzian layer rsa’’, dated to 41.9–40.6 ky cal BP (Douka et al., 2014; Wood et al., 2012). The Aurignacian cultural sequence is thus well-constrained chronologically between the last Uluzzian and the CI (39.85 ± 0.14 ka BP). This suggests that layers rsa’ars accumulated over a relatively short period, likely just a few centuries (Giaccio et al., 2008). Castelcivita is, therefore, together with Grotta Paglicci in Gargano (Palma di Cesnola, 2004), a unique case study to investigate the initial stages and early development of the Aurignacian in southern Italy. The stratigraphic succession of Castelcivita was further revisited in 2020 by I. Martini, adopting a facies analysis-based approach. Layer rsa’ is made of fine-grained reddish sediments dominated by silt and sand with scattered small limestone debris (2-4 cm in size). The limestone debris results from the cave’s roof degradation, while the sandy sediments are linked to infiltration processes (sensu Martini et al., 2018; Martini et al., 2021) from the outer area. This layer is 12–15 cm thick and, generally, in direct continuity with the Uluzzian from layer rsa’’, except for a few discontinuous thin sterile sandy lenses, separating the two levels in some areas. The subsequent layer, gic, has a thickness of around 20 cm and is well distinguishable from rsa’ due to its yellowish color and the presence of extensive concretions. Finally, layer ars was excavated over a very limited area because the upper sequence was almost completely disrupted during the construction of an artificial entrance for visitor access to the cave. The sediment composition is similar to that of rsa’, primarily sandy but with a lighter color (i.e., orange). The excavations revealed a few features across the Aurignacian sequence. In the area excavated by Gambassini, a part of an extended surface with fire was identified at the base of layer gic, in square H14 between 80 and 90 cm deep. The remaining portion of this surface was brought to light in 2016 in square H15, thus attesting to the presence of a large or two smaller adjacent fireplaces (Supplementary Fig. S1). The fireplace/s, around 2x1 m in total, was/were composed of a layer of ash (above) and a layer of charcoal (below) and was/were lying directly on the surface of the ground, partially in contact with the underlying layer rsa’. Gambassini’s excavations did not report any traces of fireplaces in layer rsa’, except for a cluster of charcoal found in square H13. However, recent investigations have also revealed similar features to gic in this layer. The excavations conducted between 1975 and 1988 employed a stratigraphic method. Findings were meticulously documented using a grid coordinate system, which consisted of square meters further subdivided into 50x50 cm sectors. Layers were systematically excavated using 10 cm deep spits, further divided into 5 cm sub-spits. Excavators paid close attention to identified discontinuities within each spit, allowing them to follow sloping deposits and anthropogenic features accurately. All archaeological findings were precisely assigned to a specific square, sector, layer, and sub-spit. Furthermore, a significant portion of the materials were spatially documented in three dimensions using elevation (Z) coordinates. All sediments were carefully dried sieved and subsequently subjected to wet sieving with a 1 mm mesh. Further screening was then performed to isolate and categorize all finds.

Figure S1. Plan view of the excavation of layer gic (spit 8) depicting evidence of the large anthropogenic feature/s (in black) identified in squares H14 and H15. The other excavated squares are colored yellow. Please, refer to the provided legend for interpreting the drawing.


The environmental and ecological setting

The environmental and ecological setting of Castelcivita was comprehensively examined by studying large and small mammals, avifauna, ichtyofauna, anthracological remains, and the sedimentary composition of the sequence (see papers in Gambassini, 1997). The evidence gathered highlights significant changes during the transition from rsa’ to gic. Layer rsa’ contains a fauna assemblage that is notably similar to the last Uluzzian of layer rsa’’, marked by the predominance of horses and a significant presence of Microtus arvalis/agrestis and Microtus (Terricola) savii. Even if rare, taxa related to milder or more humid conditions are still present (e.g. fallow deer, Apodemus sp. or Elyomys). The avifauna, on the other hand, is characterized by a high frequency of steppe grassland species, particularly those associated with rocky environments. These findings, alongside the sedimentary composition of the layer, collectively suggest relatively cold and arid conditions, featuring open environments with sparse woodlands. Climatic features do not look to be extreme, as indicated by the presence of taxa more related to Mediterranean conditions. In layer gic, there is a discernible shift in climate towards a more humid, cold-temperate environment. This is accompanied by a reduction in the presence of the horse and an increase in red deer, roe deer, and chamois. Additionally, there is a slight rise in forest and water bird species (Fiore et al., 2020; Gambassini, 1997). Finally, layer ars records a new cold phase, evident particularly in the composition of anthracological remains dominated by Pinus and Betula. Integrated studies are ongoing to establish a correlation between the local environmental signal detected across the stratigraphic sequence and supra-regional climatic changes. Some evidence seems to indicate that Heinrich Event 4 (H4) commenced slightly before the deposition of the CI tephra (Badino et al., 2020; Lowe et al., 2012; Margari et al., 2009; Wulf et al., 2018; Wutke et al., 2015). In northern Italy, such as at Fumane Cave, H4 has been identified based on micro- and macro-fauna evidence (López-García et al., 2015). Marín-Arroyo et al. (2023), for instance, associated the reduced frequency of red deer in layer D3 with the onset of H4. Although the fauna assemblage composition may reflect hunting choices made by Aurignacian foragers (Discamps et al., 2011), significant oscillations in the frequency of red deer and horse at Castelcivita likely indicate changes in the surrounding environment of the cave. It is noteworthy, however, that red deer exhibit great ecological plasticity and can adapt to steppe environments, though they prefer patchy wooded areas (Discamps et al., 2011). At Castelcivita, two cold phases could be associated with the onset of H4. The first is linked with the driest phase of the sequence detected in the late Uluzzian (rsa’’) and the Protoaurignacian (rsa’) layers. Despite internal climatic oscillations being recorded across the period of H4 (Margari et al., 2009; Skinner & Elderfield, 2007), the amelioration detected in layer gic is rather marked and suggests that rsa’’rsa’ accumulated more likely during the short-lived cold stadial GS 9/10, with layer gic corresponding to the GI 9 (Andersen et al., 2006; Rasmussen et al., 2014; Svensson et al., 2008). In this framework, H4 would only start in the uppermost layer ars. While an accurate taphonomic study is still in progress, we can state that humans played a significant role in the accumulation of mammal bones in the Aurignacian layers. Notably, evidence of carnivore activity, such as gnawing marks, is relatively low in comparison to the preceding Mousterian and Uluzzian (Romandini et al., 2020). This pattern slightly differs for birds, where there are fewer anthropogenic modifications, but several marks produced by carnivores. It is important to consider that the reduced exploitation of birds and fishes compared to the Uluzzian layers may be linked to differences in site use, a factor that warrants further exploration (Fiore et al., 2020).


Results

Raw material variability

The most prevalent raw material at Castelcivita is the local fine-grained chert, with comparable frequencies (approximately 90%) observed across the sequence. In our studied sample, quartzite, radiolarite, and coarse-grained chert were utilized at relatively low frequencies (Tables S1 and S2). Raw materials could be procured in the form of large-sized blocks from primary or sub-primary sources in the vicinity of the cave, as well as river pebbles, which were locally available along the riverbed of the Calore stream (Gambassini, 1997; Rossini et al., 2022). Unworked raw material blocks were not uncovered within the excavated area. Both tested (n = 2) and initial (n = 17) cores suggest that foraging groups opted to transport thick flakes and chunks, likely having shattered the raw material blocks away from the site.


Layer
Raw material ars gic rsa’
Coarse-grained chert 3 (3.0%) 17 (1.4%) 34 (2.6%)
Fine-grained chert 89 (88.1%) 1,125 (92.5%) 1,193 (91.5%)
Limestone 1 (1.0%) 0 (0.0%) 0 (0.0%)
Quartzite 6 (5.9%) 32 (2.6%) 59 (4.5%)
Radiolarite 2 (2.0%) 42 (3.5%) 18 (1.4%)
Total 101 (100.0%) 1,216 (100.0%) 1,304 (100.0%)

Table S1. Distribution of blanks and tools categories sorted according to raw material type. Rounded percentages are given in brackets.


Layer
Raw material ars gic rsa’
Coarse-grained chert 0 (0.0%) 1 (1.1%) 10 (8.1%)
Fine-grained chert 8 (88.9%) 89 (94.7%) 105 (85.4%)
Limestone 1 (11.1%) 1 (1.1%) 2 (1.6%)
Quartzite 0 (0.0%) 1 (1.1%) 5 (4.1%)
Radiolarite 0 (0.0%) 2 (2.1%) 1 (0.8%)
Total 9 (100.0%) 94 (100.0%) 123 (100.0%)

Table S2. Distribution of core and core-tool categories sorted according to raw material type. Rounded percentages are given in brackets.


Flake production and bipolar technology

Flakes were produced in all analyzed assemblages using platform, multidirectional, and bipolar strategies (Table S3). The latter are particularly attested in rsa’ and sharply decrease in the following layer gic (Fig. S2).


Layer Bipolar Platform Multidirectional Shatter Total
ars 1 (50.0%) 1 (50.0%) 0 (0.0%) 0 (0.0%) 2 (100.0%)
gic 15 (41.7%) 2 (5.6%) 12 (33.3%) 7 (19.4%) 36 (100.0%)
rsa’ 48 (71.6%) 9 (13.4%) 7 (10.4%) 3 (4.5%) 67 (100.0%)
Total 64 (61.0%) 12 (11.4%) 19 (18.1%) 10 (9.5%) 105 (100.0%)

Table S3. Core types associated with the production of flakes across the studied assemblages. The category Shatter contains fragments that do not retain any evidence for laminar productions. The table does not include tested cores (n = 2) as the production objective could be not assessed. Rounded percentages are given in brackets.


Figure S2. Bipolar cores found in layers gic (a) and rsa’ (b-f). The numbers shown after the letters are from the dataset created by one of us (AF). In both the dataset and the 3D model repository, they are prefixed with “CTC”, which is the common abbreviation for the site.


Most of the bipolar cores have two opposed striking platforms (Table S4) and in a few cases cores were rotated to carry on the blank production from orthogonal platforms. One of the most recurrent features of bipolar cores is the presence of at least a dihedral platform, which likely results from the disintegration of the core’s platform after several bipolar strikes (Arrighi et al., 2020; Peresani et al., 2019). Given that this platform type is usually found in only one side of the core, it has been supposed that this area was in contact with the anvil throughout the reduction (Arrighi et al., 2020). Scars of negatives can be typed in most cases as flakes and many show stepped or hinged terminations. Only in a limited number of cases bladelet-like scars are visible alongside with flake removals (n = 9, 7 in rsa’ and 2 in gic). Overall, there is no clear intention to produce bladelets in this class of cores and evidence of bipolar knapping is extremely low among the analyzed laminar blanks. Likewise, tools made on bipolar blanks are extremely rare (rsa’ = 6, gic = 1, and ars = 1) and never associated with tool types such as endscrapers, burins, and retouched blades or bladelets .


Number of platforms
Layer 1 2 3 4 Total
ars 0 (0.0%) 1 (100.0%) 0 (0.0%) 0 (0.0%) 1 (100.0%)
gic 1 (6.7%) 13 (86.7%) 0 (0.0%) 1 (6.7%) 15 (100.0%)
rsa’ 5 (10.4%) 39 (81.2%) 4 (8.3%) 0 (0.0%) 48 (100.0%)
Total 6 (9.4%) 53 (82.8%) 4 (6.2%) 1 (1.6%) 64 (100.0%)

Table S4. Number of striking platforms recorded on bipolar cores in the studied assemblages. Rounded percentages are given in brackets.


A comparison of the 3D volume of freehand and bipolar cores in both rsa’ and gic shows that bipolar cores have significantly lower values (Fig. S3), suggesting that this reduction technique allowed knappers to maximize blank production and exhaust most of the volume available. At the same time, the use of this technique can result in the split of the core in two or more bipolar shatters that are likely to be classified as bipolar cores due to the difficulty in finding a clear separation between them. This would also explain the high frequency of bipolar cores compared to freehand flake cores. Likewise, bipolar technique could also be used in an advanced stage of reduction to maximize blank production. In this regard, bipolar cores preserve less often cortical remains compared to platform and multidirectional flake cores (Table S5).


Figure S3. Comparison of the volume values of freehand (i.e., platform and multidirectional) and bipolar cores in gic and rsa’. Layer ars is not displayed as only one bipolar core is available. The figure displays also the results of the Wilcoxon tests comparing the volume values of freehand and bipolar cores within each layer.


Core type
Cortex Bipolar Platform Multidirectional Shatter
0% 48 (75.0%) 2 (16.7%) 6 (31.6%) 4 (40.0%)
1-33% 10 (15.6%) 6 (50.0%) 7 (36.8%) 2 (20.0%)
33-66% 2 (3.1%) 4 (33.3%) 5 (26.3%) 4 (40.0%)
66-99% 4 (6.2%) 0 (0.0%) 1 (5.3%) 0 (0.0%)
Total 64 (100.0%) 12 (100.0%) 19 (100.0%) 10 (100.0%)

Table S5. Percentage of cortex coverage recorded on flake cores considering all layers as a single group. Rounded percentages are given in brackets.


Initialization and configuration of blade and bladelet cores

Despite the advanced stage of reduction resulting in the discard of most cores, we managed to identify the blank types selected for knapping laminar blanks. Knappers typically selected block chunks, pebbles, and thick flakes for laminar production (Table S6). Striking platforms are consistently plain and were created by either using core tablets or positioning them on a ventral face when a flake was chosen. Faceted platforms are on the other hand absent. The main operations observed on initial cores primarily involved the decortication and shaping of the longitudinal and transversal convexities, typically executed through unidirectional strategies. Blank production often began by removing fully cortical or dihedral blanks, making use of sharp natural angles. Primary crests are also documented, and in most cases, they are one-sided, indicating that only one flank of the core was shaped with orthogonal removals. Crested blanks are relatively rare in gic (n = 3) and ars (n = 1), while they are more common in rsa’ (n = 25).


Selected blank
Layer Angular debris Blade Block Core fragment Flake Pebble Undetermined Total
ars 0 (0.0%) 0 (0.0%) 1 (14.3%) 0 (0.0%) 4 (57.1%) 1 (14.3%) 1 (14.3%) 7 (100.0%)
gic 3 (5.2%) 1 (1.7%) 4 (6.9%) 1 (1.7%) 36 (62.1%) 4 (6.9%) 9 (15.5%) 58 (100.0%)
rsa’ 0 (0.0%) 2 (3.7%) 17 (31.5%) 3 (5.6%) 14 (25.9%) 6 (11.1%) 12 (22.2%) 54 (100.0%)
Total 3 (2.5%) 3 (2.5%) 22 (18.5%) 4 (3.4%) 54 (45.4%) 11 (9.2%) 22 (18.5%) 119 (100.0%)

Table S6. Classification of cores according to the identified blank used for blade and bladelet productions. The category Undetermined includes all cores that do not retain enough information to identify the blank selected. Rounded percentages are given in brackets.


Production and maintenance operations on blade and bladelet cores

Carinated cores

Maintenance operations on carinated cores typically resulted in wide and convex flakes, often exhibiting bladelet negatives on the dorsal side. These operations aimed to isolate the flaking surface and maintain its transversal convexities, while also removing areas of the flaking surface with hinged removals. Such blanks have been identified in assemblages characterized by the presence of carinated technology (Kolobova et al., 2014; Le Brun-Ricalens, 2005). In total, we identified 110 blanks used for maintaining carinated cores. Notably, the majority of these blanks are from layer gic (n = 78, 71%). We compared the lengths of these blanks with the flaking surface of carinated cores in both gic and rsa’, observing a general intra-layer agreement (Fig. S5). This finding supports the specificity and stratigraphic attribution of carinated technology to all studied assemblages.


Figure S4. Comparison of the length of flaking surfaces of carinated cores across the studied sequence. The figure displays the results of the Kruskal-Wallis test and the pairwise comparisons.


Figure S5. Boxplots showing the distribution of length values of the flaking surfaces of carinated cores and the length of blanks identified as belonging to the maintenance of carinated cores. The figure also displays the results of the Wilcoxon tests comparing these values within gic and rsa’.


Platform cores

Bladelets are the predominant production goal across the sequence. Notably, independent blade production is only observed in gic, while simultaneous blade-bladelet production is most evident in rsa’. Simultaneous productions can also be inferred from the presence of blades with visible bladelet scars on their dorsal sides (Bon & Bodu, 2002). These scars indicate either the detachment of bladelets during blade reduction sequences (i.e., when the core allowed for blade production) or the removal of large blanks from bladelet cores, primarily for maintaining their convexities (Falcucci et al., 2017). At Castelcivita, 38 out of 42 blades with bladelet negatives identified relate to maintenance operations on bladelet cores, with the majority from layer rsa’ (71%, n = 30). This combined evidence is not surprising when considering that layer gic is primarily defined by the use of carinated technology, which rarely results in the detachment of maintenance blades (Le Brun-Ricalens, 2005).


PCA of blade and bladelet cores

Figure S6. Visualization of the results of the first and third components of the PCA conducted on laminar cores. A shows a biplot with the contribution of the different quantitative variables to the first and second components. B and C display the distribution of the studied cores in the PC1 to PC3 space, sorted according to layer (B) and core classification (C). In A, FSL stands for flaking surface length, FSL/T is the ratio between flaking surface length and thickness, FSL/W is the ratio between flaking surface length and width. The category Narrow/Burin includes narrow-sided cores and burin cores. Initial cores were excluded from the analysis.


Morphometric analysis of blades and bladelets

In this section, we will explore the morphometric analysis of complete blades and bladelets to further delve into technological variability across the sequence.

Blades

The number of available blades is relatively low compared to bladelets, primarily due to blade production not being a primary goal at the site. Constraints imposed by locally available raw materials may have played a role in that. Nonetheless, we have a statistically suitable sample for conducting a morphometric analysis of layers gic and rsa’. In both Layers, blades were produced using direct marginal percussion (Tables S7-S10 and Fig. S7). The platforms are generally plain, and their comparable dimensions suggest a uniform knapping technique. The presence of lipped internal platform edges and moderately marked bulbs suggests the use of soft hammers, whether mineral or organic. Blade production is characterized by predominantly unidirectional sub-parallel removals, with bidirectional scars being rare. Differences were not found in profile curvature, profile twisting, and blank shape (Tables S11-S14). On the other hand, the study of cross-sections suggests that the increased frequency of trapezoidal and, to a lesser extent, polyhedral shapes are in part to be linked to the frequent use of blades in rsa’ for maintaining bladelet cores (Table S15). Finally, elongation (length to width ratio) and robustness (width to thickness ratio) ratios remain stable across gic and rsa’ (Table S16, Figs. S8 and S9), whereas linear measurements show that blades from gic are shorter and narrower (Table S17 and Fig S10).


Tables and figures reporting attributes linked to the knapping technique


Layer
Platform type gic rsa’
Plain 33 (76.7%) 44 (67.7%)
Linear/Punctiform 4 (9.3%) 7 (10.8%)
Other 5 (11.6%) 10 (15.4%)
Undetermined 1 (2.3%) 4 (6.2%)
Total 43 (100.0%) 65 (100.0%)

Table S7. Platform types recorded on blades from rsa’ and gic. The category Other includes categories found in low frequencies (e.g., cortical, dihedral, double, abraded). Linear and punctiform types are grouped in a single category. A Fisher’s Exact Test reveals no differences between layers (p = 0.78).


Layer variable n mean sd min median max
gic Platform_width 41 4.188 2.683 0.2 3.80 12.5
gic Platform_thickness 41 2.037 1.576 0.2 1.70 6.4
rsa’ Platform_width 60 4.760 3.517 0.2 3.35 15.8
rsa’ Platform_thickness 60 2.188 1.942 0.1 1.50 8.1

Table S8. Summary statistics (in mm) of the width and thickness measurements recorded on blades. SD stands for standard deviation.


Figure S7. Boxplots showing the distribution of platform width (A) and thickness (B) values in gic and rsa’. The figure is complemented by the results of the performed Wilcoxon tests, confirming the marked similarity of these attributes between layers.


Lip type
Layer Absent Moderate Pronounced Total
gic 19 (44.2%) 9 (20.9%) 15 (34.9%) 43 (100.0%)
rsa’ 32 (49.2%) 13 (20.0%) 20 (30.8%) 65 (100.0%)

Table S9. Presence and type of lips recorded on blades. A Fisher’s Exact Test reveals no differences between layers (p = 0.87).


Bulb type
Layer Absent Moderate Pronounced Total
gic 15 (34.9%) 23 (53.5%) 5 (11.6%) 43 (100.0%)
rsa’ 22 (33.8%) 36 (55.4%) 7 (10.8%) 65 (100.0%)

Table S10. Presence and type of bulbs recorded on blades. Fisher’s Exact Test reveals no differences between layers (p = 1).


Tables and figures reporting technological and morphological attributes


Scar pattern
Layer Unidirectional parallel Unidirectional convergent Bidirectional Other Total
gic 24 (55.8%) 13 (30.2%) 3 (7.0%) 3 (7.0%) 43 (100.0%)
rsa’ 28 (43.1%) 19 (29.2%) 6 (9.2%) 12 (18.5%) 65 (100.0%)

Table S11. Scar patterns recorded on the blade assemblages. The Other category includes scar patterns found in low frequencies (e.g., crossed, unidirectional transverse, and undetermined patterns). The result of a Fisher’s Exact Test reveals no differences between layers (p = 0.34).


Curvature
Layer Curved Curved slightly Straight Total
gic 22 (51.2%) 6 (14.0%) 15 (34.9%) 43 (100.0%)
rsa’ 32 (49.2%) 18 (27.7%) 15 (23.1%) 65 (100.0%)

Table S12. Presence and intensity of profile curvature recorded on complete blades. The result of a Fisher’s Exact Test reveals no differences between layers (p = 0.16).


Torsion simplified
Layer no yes Total
gic 29 (67.4%) 14 (32.6%) 43 (100.0%)
rsa’ 42 (64.6%) 23 (35.4%) 65 (100.0%)

Table S13. Presence of profile twisting recorded on complete blades. The result of a Fisher’s Exact Test reveals no differences between layers (p = 0.84).


Blank shape
Layer Sub-parallel Convergent Irregular Other Total
gic 19 (55.9%) 2 (5.9%) 9 (26.5%) 4 (11.8%) 34 (100.0%)
rsa’ 22 (37.3%) 9 (15.3%) 22 (37.3%) 6 (10.2%) 59 (100.0%)

Table S14. External shape recorded on blades. The category Other includes categories found in low frequencies (e.g., convex, comma-like). A Fisher’s Exact Test reveals no differences between layers (p = 0.27).


Cross-section
Layer Lateral steep Polyhedral Trapezoidal Triangular Total
gic 11 (25.6%) 9 (20.9%) 11 (25.6%) 12 (27.9%) 43 (100.0%)
rsa’ 12 (18.5%) 20 (30.8%) 30 (46.2%) 3 (4.6%) 65 (100.0%)

Table S15. Cross-section shape recorded on blades. A Fisher’s Exact Test reveals significant differences between layers (p = 0.002).


Layer variable n mean sd min median max
gic Elongation 34 2.5 0.4 2.0 2.4 3.8
gic Robustness 34 3.2 1.0 1.6 3.1 5.9
rsa’ Elongation 59 2.6 0.5 2.0 2.5 4.0
rsa’ Robustness 59 3.3 1.3 1.5 3.0 7.2

Table S16. Summary statistics of the elongation (length to width ratio) and robustness (width to thickness ratio) of blades. SD stands for standard deviation.


Figure S8. Boxplots showing the distribution of elongation (A) and robustness (B) ratios in gic and rsa’. The figure is complemented by the results of the performed Wilcoxon tests, confirming the similarity of these attributes between layers, especially in relation to robustness.


Tables and figures reporting metric attributes


Layer variable n mean sd min median max
gic Length 34 36.0 7.9 25.0 36.0 59.6
gic Width 34 14.6 2.4 12.1 13.6 20.8
gic Thickness 34 5.0 1.9 2.8 4.6 10.5
rsa’ Length 59 42.5 10.3 24.7 42.7 65.5
rsa’ Width 59 16.4 4.0 12.3 15.7 31.6
rsa’ Thickness 59 5.8 2.9 2.0 4.9 16.2

Table S17. Summary statistics of linear dimensions (length, width, and thickness in mm) recorded on complete blades, excluding those modified by lateral retouch. SD stands for standard deviation.


Figure S9. Boxplots showing the distribution of length (A), width (B), and thickness (C) in gic and rsa’. The figure is complemented by the results of the performed Wilcoxon tests, showing that blades from gic are shorter and narrower.


Bladelets

In contrast to blades, bladelets exhibit more noticeable variations across the sequence, and the larger number of artifacts found in ars allows us to include this layer in the comparison. Bladelet blanks were still detached using direct freehand knapping, but in gic and ars, the motion appears to have been more marginal compared to rsa’, as visible from the increased presence of linear and punctiform platforms (Tables S18-S21). Notably, significant differences in platform width and thickness are observed between rsa’ and the upper layers (Fig. S10). Bulbs are more frequently absent in gic and especially ars, whereas lipped internal platforms are more frequent. Further experimental work is required to determine if these differences are related to distinct knapping techniques used in carinated core reduction. In terms of the flaking direction recorded on the visible scars of bladelets, it is almost always unidirectional (Table S22). In gic, reduction pattern is more frequently convergent, although no differences were identified in the external morphology and distal ends in dorsal view (Tables S23 and S24, but see 2DGM analysis). Profiles are straighter in gic, while profile twisting is more common in rsa’ (Tables S25-S26). In layers ars and gic, bladelet cross-sections are often triangular (Table S27), suggesting a preference for a single core ridge to guide removal (see below). The elongation ratio confirms significant morphological variability between the upper layers and rsa’, whereas the robustness remains relatively consistent throughout the sequence (Table S28 Fig. S11). A more detailed exploration of these morphological aspects through a shape analysis, including retouched and unretouched specimens, is presented after the tool analysis. As depicted in Fig. S12, the bladelets recovered in gic and ars are smaller in terms of length, width, and thickness compared to those from rsa’ (Table S29). The differences in length values are particularly pronounced and can be associated with the increased use of carinated technology.


Tables and figures reporting attributes linked to the knapping technique


Layer
Platform type ars gic rsa’
Plain 15 (34.9%) 199 (35.3%) 140 (50.5%)
Linear 18 (41.9%) 252 (44.7%) 87 (31.4%)
Punctiform 9 (20.9%) 107 (19.0%) 32 (11.6%)
Other 1 (2.3%) 3 (0.5%) 15 (5.4%)
Undetermined 0 (0.0%) 3 (0.5%) 3 (1.1%)
Total 43 (100.0%) 564 (100.0%) 277 (100.0%)

Table S18. Platform types recorded on bladelets. The category Other includes categories found in low frequencies (e.g., cortical, dihedral, double, abraded). A Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = Chi=47.56, p < 0.01).


Layer variable n mean sd min median max
ars Platform_width 43 1.581 1.186 0.1 1.5 5.7
ars Platform_thickness 43 0.567 0.616 0.1 0.2 2.7
gic Platform_width 561 1.580 1.027 0.1 1.6 7.5
gic Platform_thickness 561 0.484 0.528 0.1 0.2 4.9
rsa’ Platform_width 271 2.060 1.358 0.1 2.0 10.0
rsa’ Platform_thickness 271 0.788 0.794 0.1 0.7 6.0

Table S19. Summary statistics (in mm) of the width and thickness measurements recorded on bladelets. SD stands for standard deviation.


Figure S10. Boxplots showing the distribution of platform width (A) and thickness (B) values across the studied sequence. The figure includes results of the Kruskal-Wallis test and the pairwise comparisons. Statistically significant differences are observed when comparing both ars and gic to rsa’.


Bulb type
Layer Absent Moderate Pronounced Total
ars 35 (81.4%) 7 (16.3%) 1 (2.3%) 43 (100.0%)
gic 355 (62.9%) 200 (35.5%) 9 (1.6%) 564 (100.0%)
rsa’ 149 (53.8%) 123 (44.4%) 5 (1.8%) 277 (100.0%)

Table S20. Presence and type of bulbs recorded on bladelets. A Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 15.14, p = 0.004).


Lip type
Layer Absent Moderate Pronounced Total
ars 2 (4.7%) 32 (74.4%) 9 (20.9%) 43 (100.0%)
gic 63 (11.2%) 419 (74.3%) 82 (14.5%) 564 (100.0%)
rsa’ 48 (17.3%) 183 (66.1%) 46 (16.6%) 277 (100.0%)

Table S21. Presence and type of lips recorded on bladelets. A Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 11.02, p = 0.03).


Tables and figures reporting technological and morphological attributes


Scar pattern
Layer Unidirectional parallel Unidirectional convergent Bidirectional Other Total
ars 23 (53.5%) 18 (41.9%) 0 (0.0%) 2 (4.7%) 43 (100.0%)
gic 237 (42.0%) 308 (54.6%) 5 (0.9%) 14 (2.5%) 564 (100.0%)
rsa’ 138 (49.8%) 119 (43.0%) 4 (1.4%) 16 (5.8%) 277 (100.0%)

Table S22. Scar patterns recorded on the bladelet assemblages. The Other category includes scar patterns found in low frequencies (e.g., crossed, unidirectional transverse, and undetermined patterns). The result of a Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 15.61, p = 0.02).


Blank shape
Layer Sub-parallel Convergent Irregular Other Total
ars 15 (39.5%) 12 (31.6%) 3 (7.9%) 8 (21.1%) 38 (100.0%)
gic 185 (40.8%) 160 (35.3%) 53 (11.7%) 55 (12.1%) 453 (100.0%)
rsa’ 117 (46.4%) 81 (32.1%) 37 (14.7%) 17 (6.7%) 252 (100.0%)

Table S23. External shape recorded on blades. The category Other includes categories found in low frequencies (e.g., convex, comma-like). A Pearson’s chi-squared test reveals no differences between layers (Chi-squared = 11.98, p = 0.06).


Distal end
Layer Convex Irregular Pointed Straight Total
ars 15 (39.5%) 1 (2.6%) 14 (36.8%) 8 (21.1%) 38 (100.0%)
gic 163 (36.0%) 39 (8.6%) 181 (40.0%) 70 (15.5%) 453 (100.0%)
rsa’ 91 (36.1%) 16 (6.3%) 94 (37.3%) 51 (20.2%) 252 (100.0%)

Table S24. Distal end shape (in dorsal view) recorded on bladelet. A Pearson’s chi-squared test reveals no differences between layers (Chi-squared = 5.28, p = 0.51).


Curvature
Layer Curved Curved slightly Straight Total
ars 18 (41.9%) 11 (25.6%) 14 (32.6%) 43 (100.0%)
gic 166 (29.4%) 166 (29.4%) 232 (41.1%) 564 (100.0%)
rsa’ 111 (40.1%) 74 (26.7%) 92 (33.2%) 277 (100.0%)

Table S25. Presence and intensity of profile curvature recorded on complete bladelet. The result of a Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 11.32, p = 0.02).


Torsion simplified
Layer no yes Total
ars 39 (90.7%) 4 (9.3%) 43 (100.0%)
gic 453 (80.3%) 111 (19.7%) 564 (100.0%)
rsa’ 198 (71.5%) 79 (28.5%) 277 (100.0%)

Table S26. Presence of profile twisting recorded on complete bladelets. The result of a Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 12.69, p = 0.002).


Cross-section
Layer Flat Lateral steep Polyhedral Trapezoidal Triangular Total
ars 4 (9.3%) 5 (11.6%) 1 (2.3%) 18 (41.9%) 15 (34.9%) 43 (100.0%)
gic 22 (3.9%) 31 (5.5%) 28 (5.0%) 234 (41.5%) 249 (44.1%) 564 (100.0%)
rsa’ 15 (5.4%) 37 (13.4%) 23 (8.3%) 129 (46.6%) 73 (26.4%) 277 (100.0%)

Table S27. Cross-section shape recorded on bladelets. A Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 38.74, p < 0.01).


Layer variable n mean sd min median max
ars Elongation 38 2.2 0.7 1.2 2.1 4.4
ars Robustness 38 4.0 1.4 1.3 4.0 7.7
gic Elongation 453 2.4 0.8 1.1 2.2 8.7
gic Robustness 453 3.7 1.1 1.0 3.7 8.3
rsa’ Elongation 252 2.9 0.9 1.1 2.7 6.9
rsa’ Robustness 252 3.5 1.3 0.8 3.4 7.4

Table S28. Summary statistics of the elongation (length to width ratio) and robustness (width to thickness ratio) of bladelets. SD stands for standard deviation.


Figure S11. Boxplots showing the distribution of elongation (A) and robustness (B) ratios across the studied sequence. The figure includes results of the Kruskal-Wallis test and the pairwise comparisons. Statistically significant differences are observed when comparing both ars and gic to rsa’.


Tables and figures reporting metric attributes


Layer variable n mean sd min median max
ars Length 38 14.0 5.7 5.5 13.0 30.5
ars Width 38 6.5 2.4 2.8 6.2 11.3
ars Thickness 38 1.8 0.9 0.7 1.7 5.1
gic Length 453 15.5 5.6 6.3 14.5 38.2
gic Width 453 6.7 2.0 2.1 6.6 12.0
gic Thickness 453 1.9 0.8 0.6 1.7 6.2
rsa’ Length 252 20.9 7.7 6.9 19.4 44.2
rsa’ Width 252 7.3 2.2 1.9 7.4 11.9
rsa’ Thickness 252 2.4 1.4 0.7 2.1 9.1

Table S29. Summary statistics of linear dimensions (length, width, and thickness in mm) recorded on complete bladelets, excluding those modified by lateral retouch. SD stands for standard deviation.


Figure S12. Boxplots showing the distribution of length (A), width (B), and thickness (C) across the studied sequence. The figure includes results of the Kruskal-Wallis test and the pairwise comparisons. Statistically significant differences are observed when comparing both ars and gic to rsa’.


Tools

Common tools


Figure S13. Selection of tools and core-tools from rsa’. The number following the alphabetical list corresponds to the ID assigned by AF during the techno-typological analysis (refer to the provided dataset for details). The figure includes: multiple burin on prepared platform (a), busked burin (b), carinated endscraper (c), endscrapers (d-e), blade with Aurignacian retouch (f), retouched blade (g), and retouched flake (h). Drawings are from Gambassini (1997).


Figure S14. Selection of tools and core-tools from gic (a-e, g-j) and ars (f, i). The number following the alphabetical list corresponds to the ID assigned by AF during the techno-typological analysis (refer to the provided dataset for details). The figure includes: endscrapers (a, c, e), thick-nosed endscrapers (b, d), endscraper on a retouched flake (f), carinated endscraper (g), retouched blade (h), retouched flake (i), and truncaton (j). Drawings are from Gambassini (1997).


Figure S15. Carinated burins recovered in gic (a) and ars (b). The number following the alphabetical list corresponds to the ID assigned by AF during the techno-typological analysis (refer to the provided dataset for details). Drawings are from Gambassini (1997).


Blank
Layer Blade Flake Other Undetermined Total
ars 1 (8.3%) 11 (91.7%) 0 (0.0%) 0 (0.0%) 12 (100.0%)
gic 29 (27.9%) 66 (63.5%) 4 (3.8%) 5 (4.8%) 104 (100.0%)
rsa’ 34 (34.3%) 58 (58.6%) 3 (3.0%) 4 (4.0%) 99 (100.0%)

Table S30. Classification of common tools according to the type of blank selected. The Other category includes several carinated pieces made on pebbles and block fragments. Bladelets are excluded from the table.


Technology
Layer Optimal Initialization Semi-cortical Maintenance Other Undetermined Total
ars 18 (75.0%) 0 (0.0%) 2 (8.3%) 2 (8.3%) 0 (0.0%) 2 (8.3%) 24 (100.0%)
gic 245 (82.8%) 3 (1.0%) 21 (7.1%) 19 (6.4%) 5 (1.7%) 3 (1.0%) 296 (100.0%)
rsa’ 181 (79.4%) 4 (1.8%) 19 (8.3%) 17 (7.5%) 4 (1.8%) 3 (1.3%) 228 (100.0%)

Table S31. Classification of common tools according to the technological classification of blanks. The Other category includes several carinated pieces made on pebbles and block fragments. Bladelets are excluded from the table. Rounded percentages are provided in brackets.


Retouched bladelets

Bladelets were predominantly selected from blanks belonging to optimal reduction sequences, and only four bladelets had cortical remains (Tables S32–S33). Castelcivita has provided an exceptional number of complete retouched bladelets, especially in layer gic (Table S34), representing one of the highest proportions of complete bladelets within the Aurignacian (Falcucci et al., 2018). This variation is not related to selective recovery strategies during archaeological excavations, nor to sorting biases. All lithics were systematically sorted by Gambassini (1997) and then by one of us (AF) without applying any size cut-off. Various scenarios may explain the high frequency of complete bladelets, ranging from minimal trampling and post-depositional reworking of the sequence to specific site-use strategies. However, one of the most likely scenario is that the miniaturized size of bladelets may have prevented them from breaking. This is supported by the fact that most of the complete bladelets come from the uppermost layers, but also by the more common occurrence of mesial fragments in rsa’. In this regard, it is noteworthy that mesial fragments are more common in rsa’, likely reflecting differences in size between assemblages. Future research will address these open questions, considering spatial and functional data.


Technology
Layer Optimal Semi-cortical Maintenance Total
ars 11 (91.7%) 0 (0.0%) 1 (8.3%) 12 (100.0%)
gic 188 (97.9%) 1 (0.5%) 3 (1.6%) 192 (100.0%)
rsa’ 125 (96.9%) 2 (1.6%) 2 (1.6%) 129 (100.0%)

Table S32. Technological classification of bladelets selected for retouching. Rounded percentages are provided in brackets.


Cortex
Layer 0% 1-33% 33-66% Total
ars 11 (91.7%) 1 (8.3%) 0 (0.0%) 12 (100.0%)
gic 191 (99.5%) 0 (0.0%) 1 (0.5%) 192 (100.0%)
rsa’ 127 (98.4%) 1 (0.8%) 1 (0.8%) 129 (100.0%)

Table S33. Classification of retouched bladelets according to cortex coverage. Rounded percentages are provided in brackets.


Preservation
Layer Complete Proximal Mesial Distal Total
ars 5 (41.7%) 3 (25.0%) 1 (8.3%) 3 (25.0%) 12 (100.0%)
gic 111 (57.8%) 37 (19.3%) 10 (5.2%) 34 (17.7%) 192 (100.0%)
rsa’ 25 (19.4%) 46 (35.7%) 35 (27.1%) 23 (17.8%) 129 (100.0%)

Table S34. Classification of retouched bladelets according to the degree of fragmentation. Rounded percentages are provided in brackets. A Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 62.40, p < 0.01).


Retouch position
Layer Alternate Direct Inverse Total
ars 0 (0.0%) 8 (66.7%) 4 (33.3%) 12 (100.0%)
gic 11 (5.8%) 173 (90.6%) 7 (3.7%) 191 (100.0%)
rsa’ 26 (20.8%) 22 (17.6%) 77 (61.6%) 125 (100.0%)

Table S35. Position of retouch on bladelets across the studied assemblages. Rounded percentages are provided in brackets. A Pearson’s chi-squared test reveals significant differences between layers (Chi-squared = 176.33, p < 0.01).


Layer Bilateral Unilateral Total
ars 4 (50.0%) 4 (50.0%) 8 (100.0%)
gic 109 (63.0%) 64 (37.0%) 173 (100.0%)
rsa’ 10 (45.5%) 12 (54.5%) 22 (100.0%)

Table S36. Position of the direct retouch across the modified bladelets. Rounded percentages are provided in brackets.


Figure S16. Selection of retouched bladelets from gic. The number following the alphabetical list corresponds to the ID assigned by A. Falcucci during the techno-typological analysis (refer to the provided Dataset for details). Tools have been sorted by retouch position: direct bilateral retouch (a-d, h-i, m, p-z, and aa), direct unilateral (e-g, l, and o), inverse (j), and alternate (k and n). Photos by A. Falcucci.


Figure S17. Selection of retouched bladelets from ars. The number following the alphabetical list corresponds to the ID assigned by A.Falcucci during the techno-typological analysis (refer to the provided dataset for details). Tools have been sorted by retouch position: direct bilateral retouch (a), direct unilateral (b), and inverse (c). Photos by A. Falcucci.


Layer variable n mean sd min median max
ars Length 5 19.7 15.2 9.3 13.3 46.1
gic Length 111 13.2 5.0 5.0 11.7 34.7
rsa’ Length 25 19.9 7.2 6.0 20.0 37.5

Table S37. Summary statistics of the length values (in mm) recorded on complete retouched bladelets. SD stands for standard deviation.


Layer variable n mean sd min median max
ars Width 12 6.4 2.6 4.0 5.0 11.5
ars Thickness 12 2.0 1.1 0.8 1.8 4.3
gic Width 192 5.1 1.6 2.2 4.8 11.1
gic Thickness 192 1.6 0.6 0.5 1.5 5.3
rsa’ Width 129 6.5 2.0 2.7 6.4 11.5
rsa’ Thickness 129 1.8 0.7 0.5 1.7 4.1

Table S38. Summary statistics of linear dimensions (width, and thickness in mm) recorded on retouched bladelets. SD stands for standard deviation.


Figure S18. Boxplots showing the distribution of width (A) and thickness (B) values of retouched bladelets across the studied sequence. The figure includes results of the Kruskal-Wallis test and pairwise comparisons. Statistically significant differences are observed when comparing gic to rsa’. However, it’s important to note that the number of available retouched bladelets in ars is very small, making it challenging to draw meaningful conclusions concerning the obtained pairwise comparisons.


2DGM analysis


Figure S19. Scree plot showing the proportion of variance explained by the first four principal components.


Figure S20. Scatterplot showing the correlation (Spearman test) between the length and PC1 across the studied assemblage of complete bladelets.


Mean shapes

Studying the mean shapes is important to highlight the association between the modified and unmodified bladelets recovered in the studied layers. The comparison between unmodified bladelets and bladelets with direct retouch from gic is rather straightforward and illustrates the knappers’ interest in obtaining a convergent distal edge. On the other hand, bladelets with direct retouch from rsa’ appear to be wider, especially on the proximal side, compared to the mean shape of unretouched blanks from the same layer. These findings are further supported by the principal component analysis (PCA) visualized in the paper. The variability identified along PC1 in the bivariate plot in Fig. S18 highlights the effects of retouching on the overall shape of artifacts, as well as the close relation between blanks from gic and all bladelets modified by direct bilateral retouch.


Figure S21. Confusion matrix displaying comparisons between mean shapes of bladelet groups analyzed using 2DGM. The gray shapes in the diagonal represent the mean shapes for each group, while the comparisons are colored yellow (along the x axis) and red (along the y axis). The asterisk symbol marks statistically significant comparisons based on pairwise tests conducted following the PERMANOVA test. The plot was first generated in R using the Momocs package (Bonhomme et al., 2014) and then redrawn in Adobe Illustrator to improve the quality and readability. The raw PDF file is available in the associated research compendium.


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